Calculating a building structure and a camera position using pictures of a building photographed by a camera can be described as a practice in imaged-based modeling. Not only such a practice has been carried out in a number of research works in the field of computer vision but also it can be used widely in computer graphics, virtual reality and robotics.
The conventional methods of image-based modeling can be categorized into two groups. First, methods based on features extract corresponding feature points from many images and restore the three-dimensional positions of these points and cameras. Second, methods based on primitives take geometric primitives like balls, cylinders, cones or cubes as building blocks for more complex models and carry out a restoration procedure by projecting them again on images as parameters of the models are varied to minimize errors. Primary benefits are precise results with a relatively smaller number of images.
These conventional methods have shortcomings difficult to overcome. The feature-based methods need a relatively great number of images in order to achieve a certain degree of accuracy, whereas the primitive-based methods cannot be applied with ease to general scenes where the primitives are absent.